Stability conditions of Hopfield ring networks with discontinuous piecewise-affine activation functions

International audienceRing networks, a particular form of Hopfield neural networks, can be used in computational neurosciences in order to model the activity of place cells or head-direction cells. The behaviour of these models is highly dependent on their recurrent synaptic connectivity matrix and on individual neurons' activation function, which must be chosen appropriately to obtain physiologically meaningful conclusions. In this article, we propose some simpler ways to tune this synaptic connectivity matrix compared to existing literature so as to achieve stability in a ring attractor network with a piece-wise affine activation functions, and we link these results to the possible stable states the network can converge to

Hysteresis thresholding for Wavelet denosing applied to P300 single-trial detection

Template-based analysis techniques are good candidates to robustly detect transient temporal graphic elements (e.g. event-related potential, k-complex, sleep spindles, vertex waves, spikes) in noisy and multi-sources electro-encephalographic signals. More specifically, we present the significant impact on a large dataset of wavelet denoisings to detect evoked potentials in a single-trial P300 speller. We apply the classical thresholds selection rules algorithms and compare them with the hysteresis algorithm presented in \cite{Ranta10hyst} which combine the classical thresholds to detect blocks of significant wavelets coefficients based on the graph structure of the wavelet decomposition

Finally, what is the best filter for P300 detection?

International audienceAccording to recent literature, the most appropriate preprocessing to improve P300 detection is still unknown or at least there is no consensus about it. Research papers refer to different low-pass filters, high-pass filters, baseline, subsampling or feature selection. In this paper, using a database with 23 healthy subjects we compare the effect on the letter accuracy (single-trial detection) provided by a linear support vector machine of a high-pass filter with cutoff frequencies from 0.1 to 1 Hz and a low-pass filter with cutoff frequencies from 8 to 60 Hz. According to this study, the best combination is for a band-pass filter of 0.1 to 15 Hz

A detailed anatomical and mathematical model of the hippocampal formation for the generation of sharp-wave ripples and theta-nested gamma oscillations

International audienceThe mechanisms underlying the broad variety of oscillatory rhythms measured in the hippocampus during the sleep-wake cycle are not yet fully understood. In this article, we propose a computational model of the hippocampal formation based on a realistic topology and synaptic connectivity, and we analyze the effect of different changes on the network, namely the variation of synaptic conductances, the variations of the CAN channel conductance and the variation of inputs. By using a detailed simulation of intracerebral recordings, we show that this model is able to reproduce both the theta-nested gamma oscillations that are seen in awake brains and the sharp-wave ripple complexes measured during slow-wave sleep. The results of our simulations support the idea that the functional connectivity of the hippocampus, modulated by the sleep-wake variations in Acetylcholine concentration, is a key factor in controlling its rhythms

On source space resolution in EEG brain imaging for motor imagery

International audienceBrain source localization accuracy is known to be dependent on the EEG sensor placement over the head surface. In Brain-Computer Interfaces (BCI), according to the paradigm used, Motor Imagery (MI) and Steady-State Visual Evoked Potential (SSVEP) in particular, electrodes are not well distributed over the head, and their number is not standardized as in classical clinical applications. We propose in this paper a method for quantifying the expected quality of source localization with respect of the sensor placement, known as EEG montage. Our method, based on a subspace correlation metric, can be used to assess which brain sources can be distinguished (as they generate sufficiently different potentials on the electrodes), and also to identify regions/volumes in which precise source localization is impossible (i.e. all sources inside those regions could generate similar electrode potentials). In particular, for a MI dedicated montage, we show that source localization is less precise than for standard montages, although the local density of electrodes over the areas of interest is higher

Covid-19 and democracy, first cut policy analyses : country case studies

This report examines the intersection between political and policy responses to Covid-19 across 8 democracies (the UK, Germany, Romania, Bulgaria, Israel, Japan, Taiwan, and the US). In doing, it provides first-cut analyses of the early stages of the Covid-19 pandemic. For democracy to thrive, accountability is key. Core to this accountability is an understanding of how democratic states act to protect their citizens against a myriad of threats. In recent months, perhaps the largest of these threats has been the Covid-19 pandemic. Among the report's 8 case studies, some states were more prepared for such an event, and acted with more forethought, than others. This report shows that these differences in preparedness and forethought had real-world effects

Traitement et analyse de signaux sonores physiologiques : application à la phonoentérographie

aThe goal of this research is the development of an abdominal sound analysis system. The long-term objective is a diagnostic aid system based upon phonoenterogram analysis. The first step is the study of a specifie multi-channel acquisition system. The following stage is the preprocessing : we implement an original wavelet fixed-point algorithm for the abdominal sounds detection, segmentation and denoising. A second preprocessing step introduces available a priori knowledge on physical characteristics of isolated abdominal sounds (main frequency, duration, acoustic intensity) and studies the problem of sound source localization. The extracted physical features, as well as a set of activity indexes appropriated for the global description of the phonoenterograms (number of sounds, mean energy, etc.), are used in the last processing stage, the signal analysis. The employed techniques are principal component analysis and unsupervised clustering.L'objectif de ce travail de recherche est le développement d'un système d'étude de sons, plus particulièrement dédié à la phonoentérographie, qui devrait aboutir à plus long terme à un outil d'aide au diagnostic. Le première étape présente une chaîne d'instrumentation multi-voies spécifique. Elle est suivie par le pré-traitement: la détection, la segmentation et le débruitage par ondelettes sont réalisés avec un algorithme original optimisé par une méthode de point-fixe. Une deuxième phase introduit des connaissances a priori sur les sons abdominaux et étudie leur localisation spatiale. Les caractéristiques physiques (fréquence, intensité, durée) décrivent les sons individuellement. L'étude globale des phonoentérogrammes est réalisée à partir d'indices d'activité (nombre d'évènements, énergie moyenne, etc.). Les caractéristiques physiques et les indices sont utilisés dans l'analyse statistique des signaux, par analyse en composantes principales et classification non supervisée